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Yuan Meng

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Computer Vision Artificial Intelligence cond-mat.mes-hall Databases Distributed, Parallel, and Cluster Computing eess.SY physics.optics quant-ph Systems and Control

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preprint2026arXiv

SciIF: Benchmarking Scientific Instruction Following Towards Rigorous Scientific Intelligence

As large language models (LLMs) transition from general knowledge retrieval to complex scientific discovery, their evaluation standards must also incorporate the rigorous norms of scientific inquiry. Existing benchmarks exhibit a critical blind spot: general instruction-following metrics focus on superficial formatting, while domain-specific scientific benchmarks assess only final-answer correctness, often rewarding models that arrive at the right result with the wrong reasons. To address this gap, we introduce scientific instruction following: the capability to solve problems while strictly adhering to the constraints that establish scientific validity. Specifically, we introduce SciIF, a multi-discipline benchmark that evaluates this capability by pairing university-level problems with a fixed catalog of constraints across three pillars: scientific conditions (e.g., boundary checks and assumptions), semantic stability (e.g., unit and symbol conventions), and specific processes(e.g., required numerical methods). Uniquely, SciIF emphasizes auditability, requiring models to provide explicit evidence of constraint satisfaction rather than implicit compliance. By measuring both solution correctness and multi-constraint adherence, SciIF enables finegrained diagnosis of compositional reasoning failures, ensuring that LLMs can function as reliable agents within the strict logical frameworks of science.

preprint2026arXiv

Test-time Sparsity for Extreme Fast Action Diffusion

Action diffusion excels at high-fidelity action generation but incurs heavy computational costs owing to its iterative denoising nature. Despite current technologies showing promise in accelerating diffusion transformers by reusing the cached features, they struggle to adapt to policy dynamics arising from diverse perceptions and multi-round rollout iterations in open environments. We propose test-time sparsity to tackle this challenge, which aims to accelerate action diffusion by dynamically predicting prunable residual computations for each model forward at test time. However, two bottlenecks remain in this paradigm: 1) repetitive conditional encoding and pruning offset most potential speed gains, and 2) the features cached from previous denoising timesteps cannot constrain large pruning errors under aggressive sparsity. To address the first bottleneck, we design a highly parallelized inference pipeline that minimizes the non-decoder delay to milliseconds. Specifically, we first design a lightweight pruner that shares the encoder with the diffusion transformer. Then, we decouple the encoding and pruning from the autoregressive denoising loop by processing all denoising timesteps in parallel, and overlap the pruner with the decoder forward inference through asynchronism. To overcome the second bottleneck, we introduce an omnidirectional reusing strategy, which achieves 95% sparsity by selectively reusing features cached from the current forward, previous denoising timesteps, and earlier rollout iterations. To learn the rollout-level reusing strategies, we sample a few action trajectories to supervise the sparsified diffusion step by step. Extensive experiments demonstrate that our method reduces FLOPs by 92% and accelerates action generation by 5x, achieving lossless performance with an inference frequency of 47.5 Hz. Our code is available at https://github.com/ky-ji/Test-time-Sparsity.

preprint2022arXiv

Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform

Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck in realizing parallel MCTS on CPUs. In this work, we develop a scalable CPU-FPGA system for Tree-Parallel MCTS. We propose a novel decomposition and mapping of MCTS data structure and computation onto CPU and FPGA to reduce communication and coordination. High scalability of our system is achieved by encapsulating in-tree operations in an SRAM-based FPGA accelerator. To lower the high data access latency and inter-worker synchronization overheads, we develop several hardware optimizations. We show that by using our accelerator, we obtain up to $35\times$ speedup for in-tree operations, and $3\times$ higher overall system throughput. Our CPU-FPGA system also achieves superior scalability wrt number of parallel workers than state-of-the-art parallel MCTS implementations on CPU.

preprint2022arXiv

An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection

Image-based 3D detection is an indispensable component of the perception system for autonomous driving. However, it still suffers from the unsatisfying performance, one of the main reasons for which is the limited training data. Unfortunately, annotating the objects in the 3D space is extremely time/resource-consuming, which makes it hard to extend the training set arbitrarily. In this work, we focus on the semi-supervised manner and explore the feasibility of a cheaper alternative, i.e. pseudo-labeling, to leverage the unlabeled data. For this purpose, we conduct extensive experiments to investigate whether the pseudo-labels can provide effective supervision for the baseline models under varying settings. The experimental results not only demonstrate the effectiveness of the pseudo-labeling mechanism for image-based 3D detection (e.g. under monocular setting, we achieve 20.23 AP for moderate level on the KITTI-3D testing set without bells and whistles, improving the baseline model by 6.03 AP), but also show several interesting and noteworthy findings (e.g. the models trained with pseudo-labels perform better than that trained with ground-truth annotations based on the same training data). We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting. The codes, pseudo-labels, and pre-trained models will be publicly available.

preprint2020arXiv

Ultrafast two-photon emission in a doped semiconductor thin film

As a high-order quantum transition, two-photon emission has an extremely low occurrence rate compared to one-photon emission, thus having been considered a forbidden process. Here, we propose a scheme that allows ultrafast two-photon emission, leveraging highly confined surface plasmon polariton modes in a degenerately-doped, light-emitting semiconductor thin film. The surface plasmon polariton modes are tailored to have simultaneous spectral and spatial overlap with the two-photon emission in the semiconductor. Using degenerately-doped InSb as the prototype material, we show that the two-photon emission can be accelerated by 10 orders of magnitude: from tens of milliseconds to picoseconds, surpassing the one-photon emission rate. Our result provides a semiconductor platform for ultrafast single and entangled photon generation, with a tunable emission wavelength in the mid-infrared.